The present invention concerns the tracking of moving objects on land, in the air or at sea in a space observed by one or more sensors.
The sensors may be of the radar, radiometer, imager, or sonar type. A sensor divides the observed space in a grid which is peculiar to it and which, in the case of radar, is a three-dimensional grid enabling the position of the moving object to be defined with respect to elevation angle, azimuth and distance within resolution cells.
The known tracking methods use detection, filtering and optionally classification functions.
For each sensor, and at the time of each observation, the detection function allocates the presence of the moving object being tracked to a resolution cell of the grid, referred to as the "pip". This allocation, established in accordance with predefined criteria, for example, in the case of radar the level of the signal reflected by the moving object, makes it possible to reduce the number of items of information to be processed.
The filtering function associates the pips detected during successive observations. The association of these successive pips constitutes a "track". Amongst the most widely known filtering functions can be cited those using the Kalman-type filter for tracking limited to one moving object, or variants more suitable for an operational context with high densities of moving objects such as, for example, the one using the probabilistic data association filter (PDAF).
The classification function can make it possible to track solely one moving object of known identity from among a set of moving objects previously characterised during a learning stage.
It may be advantageous to use several sensors in conjunction. The matching of measurements coming from different sensors is then envisaged before or after filtering, which is referred to respectively either as the "merging of pips" or the "merging of tracks". The technique used generally consists, for each sensor, of taking local decisions concerning the allocation of a moving object to a resolution cell, which gives an estimation of pips, peculiar to each sensor, filtering the pips thus estimated in order to form tracks, and then merging either the selected pips or the different tracks.
However, taking successive local decisions entails a loss of information at each level of processing of the measurements, which is prejudicial to the reliability of the final decision, coming from the merging of all the intermediate decisions.